传感器网络中过滤机制下高效top-k查询处理技术

Energy-efficient top-k Query Processing Based on Filters in Wireless Sensor Networks

如何能量高效的进行top-k查询处理是无线传感器网络领域中的一个重要课题.节点设置过滤窗口可以避免与top-k查询无关的数据上传到汇聚节点或者基站,因而大大减少传感器网络的通信量,节省传感器节点能量.然而,已有算法如FILA、DAFM,基站到传感器节点的过滤窗口更新中仍然存在很大开销.提出一种基于预测信息更新窗口的top-k查询算法FAPU,该算法根据历史数据采用ARIMA时间序列预测模型对接下来s个时刻的传感器数据进行预测,根据预测信息进行多步窗口更新的代价评估,避免不必要的窗口更新,从而减小窗口更新的能量消耗.实验结果表明在确保top-k查询准确性的同时,本文所提出的FAPU算法与已有算法相比更加能量有效.

Processing top-k query in an energy-efficient manner is an important topic in wireless sensor networks. It can keep sensor nodes from transmitting redundant data to base station by installing thresholds on nodes, which decreased the communication cost between the base station and sensor nodes. However, existing algorithms such as FILA, DAFM consume much energy when updating window parameters of filters. In this paper, a new top-k algorithm named FAPU is proposed by forecasting methods to update window parameters of filters. It can predict the next s sensor values based on ARIMA time series forecasting models which can be learned by historical data. By estimating the cost of updating window parameters based on predicted information, unnecessary updates of window parameters can be avoided. Thus, the cost of updating window parameters is decreased. Experimental results show that our FAPU algorithm is more energy-efficient than existing algorithms while ensuring the accuracy of top-k query results.